We present the details of a hierarchical parallelization strategy, which provides higher efficiency than the previous parallelization approaches for MLFMA. We demonstrate the effectiveness of our algorithm on sphere problems involving large numbers of unknowns, such as a sphere of radius 120 lambda discretized with 53 million unknowns.